/*
 * Copyright (c) 2020-2023, NVIDIA CORPORATION.  All rights reserved.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

#pragma once

#include "cutlass_extensions/gemm_configs.h"
#include "tensorrt_llm/common/cudaUtils.h"

namespace tensorrt_llm
{
namespace kernels
{
namespace cutlass_kernels
{

std::vector<tensorrt_llm::cutlass_extensions::CutlassGemmConfig> get_candidate_configs(
    int sm, const bool is_weight_only, const bool simt_configs_only, const bool int8_configs_only = false);

tensorrt_llm::cutlass_extensions::CutlassGemmConfig estimate_best_config_from_occupancies(
    const std::vector<tensorrt_llm::cutlass_extensions::CutlassGemmConfig>& candidate_configs,
    const std::vector<int>& occupancies, const int64_t m, const int64_t n, const int64_t k, const int64_t num_experts,
    const int split_k_limit, const size_t workspace_bytes, const int multi_processor_count, const int is_weight_only);

} // namespace cutlass_kernels
} // namespace kernels
} // namespace tensorrt_llm
